Nonlinear Mixed-Effects Modeling

Maximum likelihood estimation of population parameters


sbiofitmixedFit nonlinear mixed-effects model (requires Statistics and Machine Learning Toolbox software)
sbionlmefitEstimate nonlinear mixed effects using SimBiology models (requires Statistics and Machine Learning Toolbox software)
sbionlmefitsaEstimate nonlinear mixed effects with stochastic EM algorithm (requires Statistics and Machine Learning Toolbox software)
sbiosampleparametersGenerate parameters by sampling covariate model (requires Statistics and Machine Learning Toolbox software)
sbiosampleerrorSample error based on error model and add noise to simulation data
sbiofitstatusplotPlot status of nonlinear mixed-effects estimation


CovariateModel objectDefine relationship between parameters and covariates
groupedData Table-like collection of data and metadata
EstimatedInfo objectObject containing information about estimated model quantities
ObservableObject containing expression for post-simulation calculations
NLMEResults objectResults object containing estimation results from nonlinear mixed-effects modeling

Examples and How To

Modeling the Population Pharmacokinetics of Phenobarbital in Neonates

This example shows how to build a simple nonlinear mixed-effects model from clinical pharmacokinetic data.


Nonlinear Mixed-Effects Modeling

A mixed-effects model is a statistical model that incorporates both fixed effects and random effects.

Supported Methods for Parameter Estimation in SimBiology

SimBiology® supports a variety of optimization methods for least-squares and mixed-effects estimation problems.

Error Models

SimBiology supports the error models described in the following table.

Perform Data Fitting with PK/PD Models

SimBiology lets you estimate model parameters by fitting the model to experimental time-course data, using either nonlinear regression or mixed-effects (NLME) techniques.